Skip to main content
SpringerLink
Log in

Mental Health Analysis Using RASA and BERT: Mindful

  • Conference paper
  • First Online:
Advanced Computing (IACC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2054))

Included in the following conference series:

  • 30 Accesses

Abstract

Psychological health problems concern an approximated 92 million people universally. That's essentially 1 in 10 people in general. Therefore, it is advisable to create a chatbot to lessen the stigma as-sociated with mental health, giving people the ability to voice their problems, and filling the gap left by the lack of support systems for those who need assistance. The preceding few years have been challeng-ing for everyone all around the world. The global escalation of Covid-19 has resulted in a substantial rise in the number of people suffering from emotional health problems. As a result, people are becoming more conscious of psychological wellness because a single consultation with a psychiatrist is expensive to execute, ideas are introduced for patients to be informed of their illness before scheduling an appoint-ment. The rise in mental diseases caused by loneliness and stress inspired us to create Mindful. The bot is a tool that allows people to converse in real time using developed rule sets or the assistance of simulat-ed intelligence and machine learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fadhil, A.: Beyond patient monitoring: Conversational agents role in telemedicine & healthcare support for home-living elderly individuals. arXiv preprint arXiv:1803.06000 (2018)

  2. Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M., Kalpana Devi, S.: A self-diagnosis medical chatbot using artificial intelligence. J. Web Dev. Web Des. 3(1), 1- 7 (2018)

    Google Scholar 

  3. Rafla, A., Casey, K.: Incrementalizing RASA's open-source natural language understanding pipeline. arXiv preprint arXiv:1907.05403 (2019)

  4. Mathew, R.B., et al.: Chatbot for disease prediction and treatment recommendation using machine learning. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE (2019)

    Google Scholar 

  5. Ayanouz, S., Abdelhakim, B.A., Benhmed, M.: A smart chatbot architecture based on NLP and machine learning for health care assistance. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security (2020)

    Google Scholar 

  6. Sophia, J.J., et al.: A survey on chatbot implementation in health care using NLTK. Int. J. Comput. Sci. Mob. Comput 9 (2020)

    Google Scholar 

  7. Hussna, A.U., et al.: PRERONA: mental health Bengali Chatbot for digital counselling. In: Singh, M., Kang, D.K., Lee, J.H., Tiwary, U.S., Singh, D., Chung, W.Y. (eds.) Intelligent Human Computer Interaction, IHCI 2020. Lecture Notes in Computer Science, vol. 12615, pp. 274–286. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68449-5_28

  8. Bulla, C., et al.: A review of AI based medical assistant Chatbot. Research and Applications of Web Development and Design 3.2 (2020)

    Google Scholar 

  9. Khadija, A., Fagroud, F.Z., Achtaich, N.: AI-powered health Chatbots: toward a general architecture. Procedia Comput. Sci. 191, 355–360 (2021)

    Article  Google Scholar 

  10. Sharma, T., et al.: Code smell detection by deep direct-learning and transfer- learning. J. Syst. Softw. 176, 110936 (2021)

    Article  Google Scholar 

  11. Mai, T.N.T., Shcherbakov, M.: Enhancing Rasa NLU model for Vietnamese Chatbot. Int. J. Open Inf. Technol. 9(1), 31–36 (2021)

    Google Scholar 

  12. Salhi, I., et al.: Towards developing a pocket therapist: an intelligent adaptive psychological support Chatbot against mental health disorders in a pandemic situation. Indones. J. Electr. Eng. Comput. Sci. 23(2), 1200–1211 (2021)

    Google Scholar 

  13. Gujjar, J.P., Kumar, V.N.: Open source Chatbot development framework - RASA. Asian J. Adv. Res. 5(1), 451–453 (2022)

    Google Scholar 

  14. Crasto, R., et al.: CareBot: a mental health ChatBot. In: 2021 2nd International Conference for EmergingTechnology (INCET). IEEE (2021)

    Google Scholar 

  15. Rahman, Md.M., et al.: Disha: an implementation of machine learning based Bangla healthcare Chatbot. In: 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE (2019)

    Google Scholar 

  16. Madhu, D., et al.: A novel approach for medical assistance using a trained chatbot. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE (2017)

    Google Scholar 

  17. Hungerbuehler, I., et al.: Chatbot-based assessment of employees’ mental health: design process and pilot implementation. JMIR Form. Res. 5(4), e21678 (2021)

    Article  Google Scholar 

  18. Pandey, S., Srishti, S., Samar, W.: Mental healthcare Chatbot based on natural language processing and deep learning approaches: ted the therapist. Int. J. Inf. Technol. 14(7), 3757–3766 (2022)

    Google Scholar 

  19. Kakuma, R., et al.: Human resources for mental health care: current situation and strategies for action. Lancet 378, 1654–1663 (2011). https://doi.org/10.1016/S0140-6736(11)61093-3, https://github.com/prachi1702/Dataset_MindFul

Download references

Author information

Authors and Affiliations

  1. CSE, ASET, Amity University, Noida, 201301, India

    Rashmi Gandhi & Prachi Jain

  2. Bennett University, Greater Noida, 201310, India

    Hardeo Kumar Thakur

Authors
  1. Rashmi Gandhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prachi Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hardeo Kumar Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prachi Jain .

Editor information

Editors and Affiliations

  1. SR University, Warangal, India

    Deepak Garg

  2. COPELABS, Lusófona University, Lisbon, Portugal

    Joel J. P. C. Rodrigues

  3. Bennett University, Greater Noida, India

    Suneet Kumar Gupta

  4. Swansea University, Wales, UK

    Xiaochun Cheng

  5. Lovely Professional University, Phagwara, India

    Pushpender Sarao

  6. SITCOE Engineering College, Ichalkaranji, India

    Govind Singh Patel

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gandhi, R., Jain, P., Thakur, H.K. (2024). Mental Health Analysis Using RASA and BERT: Mindful. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2054. Springer, Cham. https://doi.org/10.1007/978-3-031-56703-2_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56703-2_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56702-5

  • Online ISBN: 978-3-031-56703-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation